Multi-source remote sensing data fusion: status and trends

Abstract

With the fast development of remote sensor technologies, e.g. the appearance of Very High Resolution (VHR) optical sensors, SAR, LiDAR, etc., mounted on either airborne or spaceborne platforms, multi-source remote sensing data fusion techniques are emerging due to the demand for new methods and algorithms. The general fusion techniques have been well developed and applied in various fields ranging from satellite earth observation to computer vision, medical image processing, defence security and so on. Despite the fast development, the techniques remain challenging for multi-source data fusion within varying spatial and temporal resolutions. This article reviews current techniques of multi-source remote sensing data fusion and discusses their future trends and challenges through the concept of hierarchical classification, i.e., pixel/data level, feature level and decision level. This article concentrates on discussing optical panchromatic and multi-spectral data fusing methods. So far, the pixel level fusion methods have mainly focused on optical data fusion; high-level fusion includes feature level and decision level fusion of multi-source data, such as synthetic aperture radar, optical images, LiDAR and other types of data. Finally, this article summarises several trends tending to broaden the application of multi-source data fusion.

Infrastructure assessment for disaster management using multi-sensor and multi-temporal remote sensing imagery

“…Pohl and Van Genderen (1998) differentiate between three different levels of image fusion: pixel level, feature level and decision level. A review of the latest research of multi-source data fusion is given in Zhang (2010), who updates these three levels of data fusion with current developments pointing to the importance of high-level fusion approaches which include feature-level and decision-level fusion. For the assessment of infrastructural objects high-level data fusion is of utmost importance, because conclusions of the status of objects are needed.…”

Novel Metric Approach Evaluation for the Spatial Enhancement of Pan-Sharpened Images

“…Image fusion techniques can be divided into three levels, namely: pixel level, feature level and decision level of representation [8][9][10]. The image fusion techniques based on pixel can be grouped into several techniques depending on the tools or the processing methods for image fusion procedure summarized as fallow: 1) Arithmetic Combination techniques: such as Bovey Transform (BT) [11][12][13]; Color Normalized Transformation (CN) [14,15]; Multiplicative Method (MLT) [17,18].…”

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

“…Nevertheless, remote sensing applications are hard to fi nd. On the other hand heterogeneous models were reported as outperforming approaches based on individual machine learning techniques in remote sensing classifi cation tasks (Qi and Huang, 2007;Engler et al, 2013;Gómez-Chova et al, 2013) and Zhang (2010) expects that ensemble learning would be adopted to highlevel fusion of multi-source remote sensing data.…”